{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*-: utf-8 -*-\n",
    "# @author: tongzi\n",
    "# @description: Scikit-Learn introduction\n",
    "# @created date: 2019/08/13\n",
    "# @last modification:: 2019/08/13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Scikit-Learn简介"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2.1 Scikit-Learn的数据表示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.数据表**  \n",
    "基本的数据表就是二维网格数据，其中每一行表示数据集中的每个样本，而列表示构成每个样本的相关特征。例如Ronald Fisher在1936年对鸢尾花的数据集的经典分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中的每行数据表示每朵被观察的鸢尾花，行数表示数据集中记录的鸢尾花总数。一般情况下，会将这个矩阵的行称为样本（samples），行数记为 n_samples 。  \n",
    "  \n",
    "同样，每列数据表示每个样本某个特征的量化值。一般情况下，会将矩阵的列称为特征（features），列数记为 n_features 。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.特征矩阵**  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.目标数组**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xb403b38>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb437128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue='species', size=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征矩阵："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_iris = iris.drop('species', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_iris.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目标数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_iris = iris['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_iris.shape"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征矩阵和目标数组的布局：  \n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2.2 Scikit-Learn的评估器API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn API主要遵守以下设计原则：  \n",
    "*统一性*  \n",
    "&emsp;&emsp;所有对象使用共同接口连接一组方法和统一的文档。  \n",
    "*内省*  \n",
    "&emsp;&emsp;所有参数值都是公共属性  \n",
    "*限制对象层级*  \n",
    "&emsp;&emsp;只有算法可以用Python类表示。数据集都用标准数据类型（Numpy数组，Pandas的DataFrame，SciPy的稀疏矩阵）表示，参数名称用标准的Python字符串。  \n",
    "*函数组合*  \n",
    "&emsp;&emsp;许多机器学习任务可以用一串基本算法实现，Scikit-Learn尽力支持这种可能。  \n",
    "*明智的默认值*  \n",
    "&emsp;&emsp;当模型需要用户设置参数时，Scikit-Learn预先定义适当的默认值。  \n",
    "  \n",
    "只要你理解了这些设计原则，就会发现 Scikit-Learn 非常容易使用。Scikit-Learn 中的所有机器学习算法都是通过评估器 API 实现的，它为各种机器学习应用提供了统一的接口。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.API基础知识**  \n",
    "Scikit-Learn评估器API的常用步骤如下：  \n",
    "  \n",
    "（1）从Scikit-Learn中导入适当的评估器类，选择模型类；  \n",
    "（2）用合适的数值对模型类进行实例化，配置模型的超参数（hyperparameter）； \n",
    "（3）整理数据，通过前面介绍的方法获取特征举证和目标数组；  \n",
    "（4）调用模型实例的*fit()*方法对数据进行拟合；  \n",
    "（5）对新数据应用模型：  \n",
    "&emsp;&emsp;在有监督学习模型中，通常使用*predict()*预测新数据的标签；  \n",
    "&emsp;&emsp;在无监督学习模型中，通常使用*transform()*或*predict()*方法转换或推断数据的性质。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.有监督学习示例：简单线性回归** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为散点数据集拟合一条直线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42) # 设置局部种子，使得产生的随机数独立于其它配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = rng.rand(50) * 10\n",
    "y = 2 * x - 1 + rng.rand(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xbbcbe48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAGDNJREFUeJzt3XuQZGV5x/Hvb2cHHdA4KAu6A+sSQy0BCaxOoWYrCRfDzQsbvEFVlCSkNpiYqGWRLKZK0PwBCV4TLC1EAiYGL1xWhFXYEqpQytssu7iLLJEgyvQSdhAWNYzl7PLkj+5Ze3pPd5/u09OnT5/fp2pqus95p/sdXZ5+5znPeV5FBGZmVh5L8p6AmZn1lwO/mVnJOPCbmZWMA7+ZWck48JuZlYwDv5lZyTjwm5mVjAO/mVnJOPCbmZXM0rwnkOSQQw6JlStX5j0NM7PC2Lx58xMRsSzN2IEM/CtXrmRqairvaZiZFYakn6Qd61SPmVnJOPCbmZWMA7+ZWck48JuZlYwDv5lZyQxkVY+Z2TDasKXCFbc/yM7dsywfH+Oi01exdvVE3+fhwG9m1gcbtlS4+KZtzM7tBaCye5aLb9oG0Pfgr0HcenFycjJcx29mw2TN5XdS2T273/HxsVEkeOqZuX3PL33jsR1/GEjaHBGTacZ6xW9m1gc7E4I+wO7Zuf2eX/Tl+4DF+0vAF3fNzPpg+fhY6rFzzwZX3P7gos3Fgd/MrA8uOn0VY6Mjqcc3+wuhF5zqMTPrg/m0TX1VzzO/3rMvt9+ok78QOuXAb2bWJ2tXTyzI22/YUuGiG+5jbu/CIpvRJeKi01ct2jwc+M3M+qBVDf8Hv3p/5qqeTjjwm5ktsnY1/P2u4/fFXTOzRXbF7Q/uC/rzZuf2LmrlTite8ZuZdajT1gvNKnQWs3KnFa/4zcw6MJ+2qeyeJfhN2mbDlkrTn2lWobOYlTuttA38kq6RtEvS9rpjX5S0tfb1iKStTX72EUnbauPcg8HMCq+btE1SDf/Y6MiiVu60kibVcy1wJfC5+QMR8bb5x5I+Ajzd4udPjognup2gmdkg6SZtk1TDn1dnTkgR+CPibkkrk85JEvBW4JTeTsvMbDAtHx9LbLbWLm2TR/VOM1lz/H8APB4RP2pyPoA7JG2WtC7je5mZ5W7Q0jbdyFrVcx5wfYvzayJip6RDgU2SdkTE3UkDax8M6wBWrFiRcVpmZotj0NI23UjVj7+W6rk1Il5ed2wpUAFeGRHTKV7jUuCXEfHhdmPdj9/MrDOd9OPPkup5LbCjWdCXdJCk588/Bk4DtieNNTOz/klTznk98G1glaRpSRfUTp1LQ5pH0nJJG2tPDwO+Jek+4HvAbRHx9d5N3czMupGmque8Jsf/LOHYTuCs2uOHgeMzzs/MzHrMd+6amZWMA7+ZWcm4SZuZDZ1Omqh12nBtGDjwm9lQadf7vtuxw8SpHjMbKp00URu0Pvn94sBvZkOlkyZqg9Ynv18c+M1sqHTS+37Q+uT3iwO/mQ2VTpqoDUPDtW744q6ZDZVOmqgNQ8O1bqRq0tZvbtJmZtaZfjVpMzOzAnLgNzMrGQd+M7OS8cVdMxtIrVoplLHNQi858JvZwGnVSgEoZZuFXnLgN7OB066VQrNzDvzpOPCb2cDpppXCsLdZ6KU0Wy9eI2mXpO11xy6VVJG0tfZ1VpOfPUPSg5IekrS+lxM3s+HVqpVCWdss9FKaqp5rgTMSjn8sIk6ofW1sPClpBPgkcCZwDHCepGOyTNbMyqFVK4WytlnopTR77t4taWUXr30i8FBt710kfQE4G/hhF69lZiWSppWCq3q6lyXH/y5J7wCmgPdFxFMN5yeAR+ueTwOvyvB+ZlYia1dPNA3mrc5Ze93ewPUp4GXACcBjwEcSxijhWNPGQJLWSZqSNDUzM9PltMzMrJ2uVvwR8fj8Y0mfAW5NGDYNHFH3/HBgZ4vXvAq4CqpN2rqZl5kNBt9gNdi6WvFLeknd0z8BticM+z5wlKQjJR0AnAvc0s37mVlxzN98Vdk9S/CbG6w2bKnkPTWrSVPOeT3wbWCVpGlJFwD/ImmbpB8AJwPvrY1dLmkjQETsAd4F3A48AHwpIu5fpN/DzAZEWfexLZI0VT3nJRz+bJOxO4Gz6p5vBPYr9TSz4VXWfWyLxN05zaynfIPV4HPgN7Oe8g1Wg8+9esysp8q6j22ROPCbWc/5BqvB5lSPmVnJOPCbmZWMA7+ZWck4x29m+3HLheHmwG9mC7Ta79abnQ8Hp3rMbIF2LRfci6f4HPjNbIF2LRfci6f4HPjNbIF2LRfci6f4HPjNbIF2LRfci6f4HPjNbIG1qye47JzjmBgfQ8DE+BiXnXPcvou37sVTfK7qMbP9tNvvFtyLp8gc+M1KrNuyTPfiKTYHfrOSSlOvb8MpzdaL10jaJWl73bErJO2Q9ANJN0sab/Kzj9S2aNwqaaqXEzezbFyWWV5pLu5eC5zRcGwT8PKI+D3gv4GLW/z8yRFxQkRMdjdFM1sMLsssr7aBPyLuBp5sOHZHbTN1gO8Ahy/C3MxsEbkss7x6Uc75F8DXmpwL4A5JmyWt68F7mVmPuCyzvDJd3JX0j8Ae4PNNhqyJiJ2SDgU2SdpR+wsi6bXWAesAVqxYkWVaZpaCyzLLSxHRfpC0Erg1Il5ed+x84ELg1Ih4JsVrXAr8MiI+3G7s5ORkTE35WrCZWVqSNqe9ltrVil/SGcA/AH/ULOhLOghYEhG/qD0+DfhQN+9nZgtt2FLhg1+9n6eemQNgfGyUS994rFfrlkrbwC/peuAk4BBJ08AlVKt4nkM1fQPwnYi4UNJy4OqIOAs4DLi5dn4p8F8R8fVF+S3MSmD+ZqtKQtXN7tk5LvryfYBr8K29VKmefnOqx2yhxputmpkYH+Oe9af0aVY2SDpJ9bhJm1kBJN1slcQ1+JaGA79ZAaQN6K7BtzQc+M0KIE1AH10i1+BbKg78ZgWQdLNVvfGxUa54y/G+sGupuDunWQH4ZivrJQd+swHX2DP/Y287wQHfMnHgNxtg7plvi8E5frMB1qxn/nu+uJU1l9/Jhi2VnGZmRebAbzbAWpVxzq/+HfytUw78ZgOsXRmnd8yybjjwmw2wdmWc4Lt1rXO+uGs2wOrLOJOas4Hv1rXOecVvNuDWrp7gnvWn8PG3neAds6wnvOI3KwjfxGW94sBvViBrV0840FtmDvxmPdJ4h61X4zaoHPjNesB32FqRpLq4K+kaSbskba879kJJmyT9qPb94CY/e35tzI9qG7SbDZ1md9i6xt4GUdqqnmuBMxqOrQe+ERFHAd+oPV9A0gup7tH7KuBE4JJmHxBmRdasln7++IYtFdZcfidHrr/NrRYsd6kCf0TcDTzZcPhs4Lra4+uAtQk/ejqwKSKejIingE3s/wFiVnjNaumXj4/tSwNVds8SuNWC5S9LHf9hEfEYQO37oQljJoBH655P146ZDZWTj16GGo7N19g7DWSDZrFv4Gr8bwEgEgdK6yRNSZqamZlZ5GmZ9c6GLRVu3FxZ8A9bwJteWS29bJcGMuu3LIH/cUkvAah935UwZho4ou754cDOpBeLiKsiYjIiJpctW5ZhWma91ypHn7SiD+CuHdUFTKs0kFkesgT+W4D5Kp3zga8kjLkdOE3SwbWLuqfVjpkVRrscfbsVfVKjNbdasDylquOXdD1wEnCIpGmqlTqXA1+SdAHwU+AttbGTwIUR8ZcR8aSkfwK+X3upD0VE40Vis4HWLke/RGJv7J/BnF/Ru9WCDRpFwj/YvE1OTsbU1FTe0zAD4Mj1tyVfmKK6cm/8UJg/ftk5xzm4W99I2hwRk2nGujunWRvNcvEjUmLQH5Ec9G2gOfCbtdEsR5+U3gF4NsJB3waaA79ZG2tXT3DZOccxMT6GgInxsX3Pk7haxwadm7SZpdCsHXJ9YzZwtY4VgwO/lU6v2ie7WseKyoHfSqXX7ZO9MYoVkXP8Virum2PmwG8l4745Zg78VjLum2PmwG8l4745Zg78NuQau2oCiTX5vkBrZeKqHhtazSp4LjvnOO5Zf0rOszPLj1f8NrRcwWOWzIHfhpYreMySOfDb0HIFj1kyB34bWq7gMUvmi7s2lOb78czO7WWktkPWhHvpmAEZVvySVknaWvf1c0nvaRhzkqSn68Z8IPuUzVqr3yMXYG/EvpW+g75ZhhV/RDwInAAgaQSoADcnDP1mRLy+2/ex8uhV18xW1TwO/Ga9S/WcCvxPRPykR69nJdPLrpmu5jFrrVcXd88Frm9y7jWS7pP0NUnH9uj9bMj0sube1TxmrWUO/JIOAN4IfDnh9L3ASyPieODfgA0tXmedpClJUzMzM1mnZQWyYUtlXz6+UTerdFfzmLXWixX/mcC9EfF444mI+HlE/LL2eCMwKumQpBeJiKsiYjIiJpctW9aDaVkRzKd4mulmld5sj1zn982qepHjP48maR5JLwYej4iQdCLVD5qf9eA9bUgkpXjmZVmle2css+YyBX5JBwJ/DPxV3bELASLi08CbgXdK2gPMAudGRGR5TxsurVI5XqWbLY5MgT8ingFe1HDs03WPrwSuzPIeNtyWj48l5vcnxscc9M0WiVs2WK58Idas/9yywXI1v6rvxY1bZpaOA7/lzhdizfrLqR4zs5Jx4DczKxkHfjOzknHgNzMrGQd+M7OSceA3MysZl3NaW40bpJx89DLu2jHjunuzgtIgts6ZnJyMqampvKdh7L9BSjMHHzjKJW841h8AZjmRtDkiJtOMdarHWmrVPbPeU8/McfFN29iwpdKHWZlZFg781lInG6F0u2OWmfWXA7+11OlGKN7X1mzwOfBbS0ndM1vxvrZmg8+B31pK2sZwzcteiBLGup2yWTG4nNPaqu+eOV/l01gL5qoes+LIvOKX9IikbZK2StqvBlNV/yrpIUk/kPSKrO9p+WlW5XPgAUsd9M0Kolcr/pMj4okm584Ejqp9vQr4VO275ajxpqy0N2E1u3jri7pmxdGPVM/ZwOdqm6x/R9K4pJdExGN9eG9L0HhTVmX3LBfftA0gMfjXf0gskdibcNOfL+qaFUcvLu4GcIekzZLWJZyfAB6tez5dO2Y5SUrXNKvBn/+QqOyeJSAx6Puirlmx9GLFvyYidko6FNgkaUdE3F13PqkAZL/oUfvQWAewYsWKHkzLmukkXdMspz8i8WyEe/WYFVDmwB8RO2vfd0m6GTgRqA/808ARdc8PB3YmvM5VwFVQ7dWTdV7W3PLxMSoJQT4pXdPsQ+LZCH58+et6PjczW3yZUj2SDpL0/PnHwGnA9oZhtwDvqFX3vBp42vn9fCXdlNUsXdMsd++cvllxZc3xHwZ8S9J9wPeA2yLi65IulHRhbcxG4GHgIeAzwF9nfE/LKOmmrMvOOS4xXdPJh4SZFYPbMltb3ZZ+mln/dNKW2XfuWlv1d+6aWfG5V4+ZWck48JuZlYwDv5lZyTjwm5mVjC/uFoira8ysF1zOWRCNjdUARkfEQQcs5enZOX8QmJWcyzmHUFLPnLm9we7ZOaB9h00zs3nO8RdEmn73zTpsmpnVc+AviLS9cbwhipm141TPgGq8kHvy0cu4cXMlsUVyPTdPM7N2vOIfQI2bn1R2z3Lj5gpveuXEvsZqBx84yuiShVsduHmamaXhFX8OksoygZbbG87O7eWuHTPcs/6Ulq/jC7tm1o7LOfsssSxziUDVKp1WBN78xMwSuZxzgCWWZT6b7sN3/MDRxZiSmZWMc/x9lqXq5ldtLuyamaXhwN9nWVbts3PPsmFLpYezMbMy6jrwSzpC0l2SHpB0v6R3J4w5SdLTkrbWvj6QbbrFl/WSim/QMrOssuT49wDvi4h7axuub5a0KSJ+2DDumxHx+gzvM1SerrVYSDIxPsbO3bOMHzjKU88kj/MNWmaWVdeBPyIeAx6rPf6FpAeACaAx8Fud5eNjVBKC98T42IJSzdUfuiMx+PsGLTPLqic5fkkrgdXAdxNOv0bSfZK+JunYXrxfkV10+irGRkcWHEu68eqSNxybapyZWacyl3NKeh5wI/CeiPh5w+l7gZdGxC8lnQVsAI5q8jrrgHUAK1asyDqtgTV/g1W7G6/SjjMz61SmG7gkjQK3ArdHxEdTjH8EmIyIJ1qNG+YbuMzMFkNfbuCSJOCzwAPNgr6kFwOPR0RIOpFqauln3b5nEbmtgpkNmiypnjXA24FtkrbWjr0fWAEQEZ8G3gy8U9IeYBY4NwaxR8QiaWzP4M1SzGwQZKnq+RbV9jGtxlwJXNntexRdUnuG+c1SHPjNLC++c3cRNau5dy2+meXJTdo60C5f33j+BWOj+/bEredafDPLkwN/Su3y9UnnR0fE6BIt6L7pWnwzy5tTPSm1ytc3Oz+3N3jec5fu2zVrYnyMy845zvl9M8uVV/wptcvXNzu/+5k5tnzgtEWbl5lZp7ziT6lZXn7+eLvzZmaDwoE/pXY9dtL24DEzy5tTPSm1653j3jpmVhTebN3MbAh00qvHqR4zs5Jx4DczK5nS5/jdPdPMyqbUgd/dM82sjIY28KdZybt7ppmV0VAG/rQreXfPNLMyGsqLu+366kD1w2GJkrcT8N22ZjbMhjLwt1vJz/9FsDfhHgbfbWtmwy5TqkfSGcAngBHg6oi4vOH8c4DPAa+kutfu2yLikSzvmcby8TEqCcF/icSR629jiZQY9Eckd880s6HX9Ypf0gjwSeBM4BjgPEnHNAy7AHgqIn4H+Bjwz92+XyeS+uYA7I0gat+TPBvhoG9mQy9LqudE4KGIeDgifg18ATi7YczZwHW1xzcAp0pNEus9tHb1BJedc9y+PvgjKd/SuX0zK4MsqZ4J4NG659PAq5qNiYg9kp4GXgQ8keF9EyWVb96z/hQAjlx/W9ufd27fzMoiy4o/aRndmENJM6Y6UFonaUrS1MzMTEcTmb9YW9k9S/Cb8s0NWypA85X8iOSdscysdLIE/mngiLrnhwM7m42RtBR4AfBk0otFxFURMRkRk8uWLetoIu3KN5v1yv/IW4/nx5e/jnvWn+Kgb2alkSXwfx84StKRkg4AzgVuaRhzC3B+7fGbgTtjEfpAtyvfbMz5e4VvZmXWdY6/lrN/F3A71XLOayLifkkfAqYi4hbgs8B/SHqI6kr/3F5MulGz8s36FM/a1RMO9GZmZKzjj4iNwMaGYx+oe/wr4C1Z3iONi05ftaBFA/hirZlZM0PRq8fbHpqZpTcUgR+cyjEzS2soe/WYmVlzDvxmZiXjwG9mVjIO/GZmJePAb2ZWMlqEG2kzkzQD/KTFkENYhEZvBeLf37+/f//yavb7vzQiUvW7GcjA346kqYiYzHseefHv79/fv79//yyv4VSPmVnJOPCbmZVMUQP/VXlPIGf+/cvNv3+5Zf79C5njNzOz7hV1xW9mZl0qXOCXdIakByU9JGl93vPpJ0lHSLpL0gOS7pf07rznlAdJI5K2SLo177n0m6RxSTdI2lH7d/CavOfUT5LeW/u3v13S9ZKem/ecFpOkayTtkrS97tgLJW2S9KPa94M7fd1CBX5JI8AngTOBY4DzJB2T76z6ag/wvoj4XeDVwN+U7Pef927ggbwnkZNPAF+PiKOB4ynR/w6SJoC/AyYj4uVUN4BalM2dBsi1wBkNx9YD34iIo4Bv1J53pFCBHzgReCgiHo6IXwNfAM7OeU59ExGPRcS9tce/oPoffal6UUs6HHgdcHXec+k3Sb8F/CHVne2IiF9HxO58Z9V3S4Gx2h7eB7L/Pt9DJSLuZv99ys8Grqs9vg5Y2+nrFi3wTwCP1j2fpmSBb56klcBq4Lv5zqTvPg78PfBs3hPJwW8DM8C/11JdV0s6KO9J9UtEVIAPAz8FHgOejog78p1VLg6LiMeguhgEDu30BYoW+JVwrHRlSZKeB9wIvCcifp73fPpF0uuBXRGxOe+55GQp8ArgUxGxGvg/uvgzv6hqueyzgSOB5cBBkv4031kVU9EC/zRwRN3zwxnyP/UaSRqlGvQ/HxE35T2fPlsDvFHSI1TTfKdI+s98p9RX08B0RMz/lXcD1Q+Csngt8OOImImIOeAm4PdznlMeHpf0EoDa912dvkDRAv/3gaMkHSnpAKoXdm7JeU59I0lU87sPRMRH855Pv0XExRFxeESspPr//Z0RUZoVX0T8L/CopFW1Q6cCP8xxSv32U+DVkg6s/bdwKiW6uF3nFuD82uPzga90+gKF2nM3IvZIehdwO9Ur+tdExP05T6uf1gBvB7ZJ2lo79v6I2JjjnKy//hb4fG3h8zDw5znPp28i4ruSbgDupVrhtoUhv4tX0vXAScAhkqaBS4DLgS9JuoDqh+FbOn5d37lrZlYuRUv1mJlZRg78ZmYl48BvZlYyDvxmZiXjwG9mVjIO/GZmJePAb2ZWMg78ZmYl8/+KUzsigD117QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb959c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按照前面的步骤逐一实现：  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1）选择模型类  \n",
    "&emsp;&emsp;在Scikit-Learn中，每一个模型都用一个Python类来表示。因此，加入我们想要计算一个简单的回归模型，直接导入线性回归模型类：  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2）实例化模型类，并配置超参数  \n",
    "&emsp;&emsp;根据不同模型的情况，我们可能需要回答以下问题：  \n",
    "&emsp;&emsp;[1]我们想要拟合偏移量（即直线的截距）吗？  \n",
    "&emsp;&emsp;[2]我们需要对模型进行归一化处理吗？  \n",
    "&emsp;&emsp;[3]我们需要对特征进行预处理以提高模型的灵活性吗？  \n",
    "&emsp;&emsp;[4]我们打算在模型中使用哪种正则化类型？  \n",
    "&emsp;&emsp;[5]我们打算多少模型组件？  \n",
    "  \n",
    "所谓**超参数**是指那些在模型实例化时必须指定的参数，即在拟合数据之前必须确定的参数。  \n",
    "对于现在这个简单的线性回归模型，我们在实例化它时需要指定*fit_intercept*这个超参数以拟合直线的截距："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression(fit_intercept=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LinearRegression的标签如下所示："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（3）将数据整理成特征矩阵和目标数组  \n",
    "虽然我们已经有了目标数组$y$（长度为$n\\_samples$的数组），但还需要将数据$x$整理成($n\\_samples$, $n\\_features$)的形式。在这个例子中，我们的数据集$x$只有一个特征值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，我们的数据集*x*并不是(*n_samples*, *n_features*)的形式，这里*n_features*=1，需要做一个简单的变换即可："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = x[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（4）用模型拟合数据  \n",
    "调用模型实例的方法*fit()*对数据进行拟合,传入特征矩阵$X$和目标数组$y$："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$fit()$方法内部进行大量计算后，将计算的结果存储在模型的属性中供用户调用。在Scikit-Learn中，所有的$fit()$方法获得的模型参数都带一条下划线。例如，在线性回归模型中，模型参数如下所示：  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.00660766])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_ # 系数（coefficient）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5350275750800026"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_ # 截距（intercept）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（5）预测新数据的标签  \n",
    "&emsp;&emsp;模型训练出来后，有监督机器学习的主要任务就变成了了对不属于训练集的新数据进行预测。在Scikit-Learn中，我们使用$predict()$方法进行预测。“新数据”是特征矩阵$X$的坐标值，我们需要用模型预测出目标数组的$y$轴坐标："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "xfit = np.linspace(-1, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xfit.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先将这些$X$值转换成($n\\_samples$, $n\\_features$)的特征矩阵形式，之后将其输入到模型中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xfit = xfit[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "yfit = model.predict(Xfit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，把原始数据$xfit$和拟合结果$yfit$可视化出来："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc5c4278>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc5c4240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(xfit, yfit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.有监督学习示例：鸢尾花数据分类**  \n",
    "&emsp;&emsp;这个示例的问题是：如何为鸢尾花数据集建立模型，先用一部分数据进行训练，再用模型预测出其它样本的标签？  \n",
    "  \n",
    "我们将使用非常简单的高斯朴素贝叶斯（Gaussian naive Bayes）方法完成这个任务，这个方法假设每一个特征中属于每一类的观测值都符合高斯分布。  \n",
    "由于需要用模型之前没有接触过的数据来评估模型的效果，因此先将数据集分割成训练集(training set)和测试集(test set)。虽然完全可以手动分割，但是使用Scikit-Learn中的$train\\_test\\_split()$函数更为方便："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris, \n",
    "                                                random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整理好数据后，用模型进行预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GaussianNB()\n",
    "model.fit(Xtrain, ytrain)\n",
    "y_model = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后用$accuracy\\_score()$方法验证模型预测结果的准确率（正确结果占总预测样本数的比例）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest, y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.无监督学习的示例：鸢尾花数据降维**  \n",
    "&emsp;&emsp;对数据进行降维，以便对数据进行可视化。由前面的介绍我们知道，鸢尾花由四个维度构成，即每一个样本有四个维度。  \n",
    "  \n",
    "&emsp;&emsp;**降维的任务是找到一个可以保留数据本质特征的低维矩阵来表示高维数据。**降维通常用于数据的可视化工作，毕竟用二维数据画图比用四维甚至更高维数据画图更方便。  \n",
    "  \n",
    "&emsp;&emsp;下面我们介绍主成分分析（principle component analysis，PCA），这是一种快速降维的技术。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同样按照前面的步骤进行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA # (1)选择模型类\n",
    "model = PCA(n_components=2) # (2)实例化模型类，并配置超参数\n",
    "model.fit(X_iris) # (3)拟合数据，注意：这里不适用目标数组y_iris\n",
    "X_2D = model.transform(X_iris) # (4)将数据转换为二维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2D.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_2D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在来画出结果。快速处理方法就是先将二维数据插入到鸢尾花的DataFrame中，然后用Seaborn的$lmplot()$方法画图:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xc97f550>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc92bb70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['PCA1'] = X_2D[:, 0]\n",
    "iris[\"PCA2\"] = X_2D[:, 1]\n",
    "sns.lmplot('PCA1', 'PCA2', hue='species', data=iris, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.无监督学习示例：鸢尾花数据聚类**  \n",
    "&emsp;&emsp;聚类是对没有任何标签的数据集进行分组。我们将使用一个强大的聚类方法--高斯混合模型(Gaussian mixure model, GMM).GMM试图将数据构造成若干服从高斯分布的概率密度函数簇："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:58: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be  removed in 0.20. Use class GaussianMixture instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function distribute_covar_matrix_to_match_covariance_type is deprecated; The function distribute_covar_matrix_to_match_covariance_typeis deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:77: DeprecationWarning: Function log_multivariate_normal_density is deprecated; The function log_multivariate_normal_density is deprecated in 0.18 and will be removed in 0.20.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.mixture import GMM # (1)选择模型类\n",
    "model = GMM(n_components=3, \n",
    "            covariance_type='full') # (2)实例化模型类，并设置超参数\n",
    "model.fit(X_iris) # (3)拟合数据，注意：不需要目标数组y_iris\n",
    "y_gmm = model.predict(X_iris) # 确定簇标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和之前一样，将簇标签插入到鸢尾花的DataFrame中，然后用seaborn可视化结果:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xdfc6a20>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfc6c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['cluster'] = y_gmm\n",
    "sns.lmplot('PCA1', 'PCA2', data=iris, hue='species', \n",
    "           col='cluster', fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.2.3应用：手写数字探索**  \n",
    "&emsp;&emsp;我们来挑战一个光学字符识别问题：手写数字识别。简单点说，这个问题包括图像中字符的定位和识别两部分。为了演示方便，我们选择使用 Scikit-Learn 中自带的手写数字数据集。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.加载并可视化手写数字**  \n",
    "首先用Scikit-Learn的数据获取接口加载数据，并统计："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 8, 8)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个数据集包含1797个样本，每一个样本是一幅$8 \\times 8$像素的图像。我们对前100张图像可视化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe803eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, 10,  # 10 * 10 的画布\n",
    "                         figsize=(8, 8), # 图片大小8 x 8\n",
    "                        subplot_kw={'xticks':[],'yticks':[]}, # 不显示横纵坐标标签\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1) # 水平和垂直间距\n",
    "                        )\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(digits.target[i]),\n",
    "            transform=ax.transAxes,color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了在Scikit-Learn中使用数据，需要一个$(n\\_samples, n\\_features)$的二维特征矩阵--可以将每个样本图像的所有像素作为特征，也就是将每个数字的$8 \\times 8$像素平铺成长度为64的一维数组。此外，还需要一个目标数组，用来表示每个数字的真实标。这两份数据已经存储在书写数据集的$data$和$target$属性中了，可以直接使用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = digits.data\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = digits.target\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.无监督学习： 降维**  \n",
    "我们无法在64维的空间中进行可视化，因此需要借助无监督学习方法将数据降到二维。这次试试使用流行学习中的ISOmap算法来降维而不是用之前的PCA算法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import Isomap # (1)选择模型类\n",
    "iso = Isomap(n_components=2) # (2)实例化模型类，并配置超参数\n",
    "iso.fit(digits.data) # (3)拟合数据\n",
    "data_projected = iso.transform(digits.data) # (4) 降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_projected.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据已经降到二维，将其进行可视化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:106: MatplotlibDeprecationWarning: The spectral and spectral_r colormap was deprecated in version 2.0. Use nipy_spectral and nipy_spectral_r instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe8036a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_projected[:, 0], data_projected[:, 1],\n",
    "            c=digits.target, edgecolor='none', alpha=0.8, \n",
    "           cmap=plt.cm.get_cmap('spectral', 10))\n",
    "plt.colorbar(label='digit label', ticks=range(10))\n",
    "plt.clim(-0.5, 9.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.数字分类**  \n",
    "先将数据集分割成训练集和测试集，然后用高斯朴素贝叶斯模型来拟合："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB # (1)选择模型类\n",
    "model = GaussianNB() # (2)无监督学习：实例化模型类\n",
    "model.fit(Xtrain, ytrain) # (3)拟合数据\n",
    "y_model = model.predict(Xtest) # (4)预测新数据\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算模型的准确率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尽管我们知道了模型的准确率，但是这个指标并没与告诉我们模型**哪里**做得不好，**混淆矩阵**(confusion matrix)可以解决这个问题:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(91.68,0.5,'true value')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sbn+Y75r1Ji7xXTNImlULZmHpBRtXBKIbFH52j+5Qp5oogIh4j602DGO/IpZC8YGI/AuoIiLXA58DrwZryzCMRMLzWlxVnxaRU4HNwF+Ah1R1bODODMNIGGK6aXcLgxUHwzhA8SwUIrIF9/kEkILT67FNVSsFacwwjMQhlluPikVfi8i5QJvAHBmGkXCUeWSmqg4HTg7AS7kIU/pyQidQJ6dQ9cVXqDroNaq99gYVrrwagPRzzqPaW+9Qa9wEpFLlxPC6F3UrVsqk/+An+Gji+wz/6j2OanWkL7phSjiH2MZR/K3IywiQBZykqsf76mQ3gkjhjnUcRVnTl2MZR5FICdQljaOQtHQ0JxuiUao+9wJbXnwedu6kYMsWqg54lo033YBuLj41OpZxFInUBrGOo3h84IPMmjqbD98dSVJyEunpaWzZvLXEz8cyjiKREs79HEdxVpGf04AtwDmxiBdFRPxZvKAIYUpfDkMCteZkOxtJSc6PKnlLfqBg7S9x+/Tb697QrZCZQau2x/DhuyMByNuZV2qRiJUwJZwXEssziqvLKioiI3bfBXQSkSqu5tll1SyO4pKH27Ru6Ye07wTl1VfdSISqLw8iWr8+2R8NJ2/Rwrj9FSUUbVCEBgfXZ+OG3+nz3AMc3qwJC+cu4qkHnyF7e05cukH4DfrfQomFQkSe54/ejj1Q1dtK0W0ALAAGuxqCc8vSvzQziZDCHRShSKAuKOC3G65DKmRS+bHHiTZqTP7yH+N0+AehaIMiRJOiNG1xOP/o3Z/vvl3APX16cc2tV/Biv0Fx6YYp4byQ0q4oZsShmwXcDtwP3KWqs0UkW1VLTXtV1UHAIIjtGUWY0pfDlECt27ayY/a3pLRuQ7aPhSJMbQCwdvU61q5Zz3ffLgBg7Mfjuabn5XHrhinhvJASn1Go6pul/ZQmqqoFqvoMcDVwv4i8QABBvmFKX070BGqpXBmpkOm8SEkhpVUW+T/7O8Ep0dtgdzas38jaVWtpdKjz4PO49lksW7w8bt0wJZwXEsuAq5o408ybAWmF+1XVs4tUVVcCF4rIX3GGgPtKmNKXEz2BOlK9OpXu7o1EnWTvnAlfsmPqFNLPO5+MbhcTqVaNaq++zo5pU9nS/5/71Ove0gXoe/8A+r70CMnJyaz8aRUP9ip27aoyEaaE80Ji6R4dAwwF7gRuxInbX6+q9/jmohhsmnlw2DRzm2ZeiJ/do9VV9TVgp6pOUNVrgLZxuTMMI1TE8id2p/t7jXsLsRqnV8MwjAOEWArF4+5SgP8HPA9UAv4eqCvDMBKKWArFN6q6CdgEdArYj2EYCUgszygmi8gYEblWRKoG7sgwjITDs1CoahPgAaA5MFNEPhaRywJ3ZhhGwlCmRYpFpAYwALhUVaOBucK6RyFcfrcM7em7JkDFbs8HolsjI5jcpV+3+z5cKFB86x4VkUoicqWIjAYmA2uw4BrDOKCI5U/WHGA48JiqTgnYj2EYCUgsheIQTdQpmYZh7BVieZhpRcIwDnBsNXPDMDwJfaE40MN1g/IK/vrNLyig23Mf0fMNZ3mYR/4ziYueHc6Fzw7nziFfsD13p4fC3vNaSGpqCqPHDWXcpP8xYcpI7rrvVl90IXzhurH0ehwuIuNEZJ77+igRecBXF+UkEokw8LknOPOsy2hxdCe6dTuXpk2bxK379tvDOOecK31w+Adh8gr++3336wU0rlVl1+s7z2zDB73OZVivc6lTJZP3p5Q/di+ots3N3cH5Z19N53bn0bn9eXTq3I5js46OWzcIv0G1wS79GD7zKnAf7uQwVZ0LXOybgziwcN1gvIK/ftdu2sbERSv5W+s/vriZaSmAE9eWuzOPmDrz94LX3dm+bTsAyclJJCUn+xIvF8Zw3VgKRYaqTtttX5lG7IhIOxG5Q0S8s+/LQHGBovXq1fHzFL4RJq/gr99/jvyGXqdn7ZHr+NCwiXR+4n1+XL+Ji09olhBedycSifD5xA+Z98Mkvho/mW9nzo1bMwi/QX+/YikUv4rIobhBuyJyAc6gqxIRkWlFtq8HXgAqAg+LyL3lt7vHefbYl6idNGHyCv75/Wrhz1TNTKdZgxp7vPfYhe0Z27sbjWtV4bO55c/mDLJtCwoKOKX932jZvBMtW7XgCB8u58MYrhtLobgF+BdwhIisAnoBN3kck1xkuwdwqqo+CnQBLi3pIBHpISIzRGRGQcE2T2MWrhscfvmd/dNaJixYwen/GMa9701g+tI19H7/j4zlaCTCaUc1Zty85fvca2ls3rSFyZOm0alzu7i19qtw3UJUdZmqngLUBI5Q1XaqutxLV0Sqikh1nPkk612tbZRy26Kqg1Q1S1WzvKL6wcJ1g8Qvv7d1zWJM726MvvdC/tH9JFofWpcnunVgxa/OnAhV5auFK2hcs/zLFQbVttWrV6VSZWfp3bS0VNqfdDxLfog/lXx/Ddd9aLfXAKjqY6UcVhmYibOeh4pIHVX9RUQy3X2+YOG6wXgN0i+AKjw4bCLbcnagwOF1q3H/ueVfoTIor7Xq1GTgy32JRqNEJMKI4Z8y9rMv49bdX8N1/6/IyzTgTGChm51ZtpOJZAC1VdWzLNvs0XD5tdmjDvvr7NFYlhT80+peIvI0sPuSgTGhqtsB/1aUMQxjr1CekZkZwCF+GzEMI3GJ5RnFd/yxBmkU56Fmac8nDMPYz4jlJvjMItt5wFpVDdeKN4ZhxEWphUJEIsAnqnrkXvJjGEYCUuozClUtAOaISDDrrxmGEQpi6R79AmgNTAN2DZdU1bODNBZE92hQhKkbM2xkr54YiG56vfaB6IYN37pHgUfj9GIYRsiJpVCcsfvK5SLyFDChhM8bhrGfEcs4ilOL2Xe630YMw0hcSryiEJGbgJuBQ0Sk6CT8isDXQRszDCNxKO3W411gNNAXKJohsUVVNwbqyjCMhKLEQlFkBfPue8+OYRiJiKVw7yXNsKRlB63rp2aX86/kvMtv4vwrb+Gia24D4LMvJnLOpTfQot0ZzFsY/zTrA7Vtd6dMixTvTWIZRxGJRFg4fyJdz+jOypVrmDplFJddfjMLF/5Q7vOWRzOWcRQnntiGbdu2M3jwALKyYosOjWUcRRBtEJRueTRLG0fR5fwrGfraQKpW+SP0ZunyFUQkwqP/HMidt1zHkU0PL/bYWMZR7O9tCz4uUlweROQ4EankbqeLyKMiMlJEnhKR8kcZ7UaY0ozDkJYdtG7QSdEAhzY6iMYHN/BFy9r2D4K69Xgd2O5uP4eTePWUu+/ffp0kjGnGfhOU3zC0rYjQ4+/3c9E1PRn20ai4vBXHgdy2uxPM2GOIFJlhmqWqx7rbk0Rktl8nCWOasd8E5TcMbfv2y/2pVbM6G377net79abxwQ3JOqZFPBb/xIHctrsT1BXFPBG52t2eIyJZ4Kw6hruQUHEkQgr3gZqWvTd0/dasVbM6ANWrVqFzhxP4bsH3cfnbnQO5bXcnqEJxHXCSiCwFmgFTRGQZzqpj15V0UCKkcB+oadl7Q9dPze3ZOWxzV/Hanp3D5GmzaHJIo7j87c6B2rbFEcithzsG4yoRqYgTm5cErFRVX/80hynNOGxp2Yneths2/sbtvfs4unn5nNGlI+3aZvH5hK/p+8zLbPx9Ezff9TBHNDmEQc88sc/9Bq27z1O49xU2zdymmYNNMw+afdo9ahjG/oUVCsMwPLFCYRiGJ1YoDMPwxAqFYRieWKEwDMMT6x49AAmiOzdsXblbp74ciG5m25sC0Q0K6x41DMM3rFAYhuGJFQrDMDyxQmEYhidWKAzD8MQKhWEYnoS+UIQpzThMukGlhoehDfILCrjo3me4td/rAFz1yEtcdO8ALrp3AKfc1Ide/d9IKL9BahYS6nEUiZRmHCbdIFLDw5YYXto4irc+mcCCZSvZmp3LC3df86f37njmTTq1as5ZHbKKPTaWcRSJ9L3d1ynct4lIwyC0ixKmNOOw6QaRGh6GNli74XcmfruI8zodt8d727JzmDZ/KZ2yjkwYv0FqFiWoW48+wDciMlFEbhaRmkGcJExpxmHTDYIwtEG/t0bw90v+SiSy5x/aL6bP47jmh5GZkVZurxCu720hQRWKZUADnILRClggIp+KyJVuPF6xlDVcN0xpxmHTDYJEb4MJsxZQrVImzQ4pfl2Q0ZNnc/oJx5RZd3fC9L0tJKi4flXVAmAMMEZEkoHTcdYxfRoo9gpDVQcBgyC2ZxRhSjMOm24QJHobzP5+OV/OWsCk2YvI3bmTbdm53PfCu/S99RJ+37KNeUt/5pk7rkwYv0FrFiWoK4o/lTdV3amqI1S1O3CQXycJU5px2HSDINHb4PbuZzD2xQcY/XxvnrrtMlo3P4y+t14CwJipc+nQsimpKckJ4zdozaIEdUXRraQ3VDXbr5OEKc04bLpBpIaHrQ2K8tmU2VxzdidftML0vS0k1N2jRvmwaeY2zbwQm2ZuGIZvWKEwDMMTKxSGYXhihcIwDE+sUBiG4YkVCsMwPDmgukebVfNtrNefWLBxRSC6YVr8OExeASqmpAeiu/K+E3zXrPzoON81C7HuUcMwfMMKhWEYnlihMAzDEysUhmF4YoXCMAxPrFAYhuFJ6AtFEMnDFStl0n/wE3w08X2Gf/UeR7WKLyOxkDClZUO4/AbhtX79uowYNYSpMz9l8vTR3HBzHKE10WTSrn6YtOseJ73HkyR3OA+ASKNmpF37GGnX9SHtigeQqrXKfQpL4S6BsiYPxzqO4vGBDzJr6mw+fHckSclJpKensWXz1hI/H8s4ikRJy4ZgErNjHUeRKOnesYyjqF27JrXr1GLunPlkZlZg/MThXNb9Jr5ftKTEY0odR5GcCjtzIRIl7YoH2DFmCKln30DOsGfRDatJatWZSN1D2PHxq386LJZxFGFN4U4RkStE5BT39SUi8oKI3OLG4vlCEMnDFTIzaNX2GD58dyQAeTvzSi0S+9IrBJOWDeHyG5TXtWvXM3fOfAC2bt3G4u+XUrdu7fIL7sx1fkeiEI0CCiiS6oT1Smo6uvW3ckkHncIdVMLVv13tDBG5EsgEPgQ6A22A+IMHKT55uE3rlnFpNji4Phs3/E6f5x7g8GZNWDh3EU89+AzZ23MSzmuQhMnv3vDa8KD6HHV0M2bOmFN+ERHSrn2MSNXa7JzxOQWrl5H7yWukdbsTzdsBudlkv/FouaSDboOgnlG0UNVuwHlAF+ACVX0buBoo0X0ipHBHk6I0bXE4H7zxId1OvZLs7dlcc+sVcWlCuNKyIVx+g/ZaoUIGb73zIvfd8zhbtsRxdalKzuAH2T6wF9F6hyA165Pcpis5Q58m+/le5M2dSMqpl5RLOug2CKpQREQkBagIZACV3f2pQIm3Hqo6SFWzVDUrEqngeZIgkofXrl7H2jXr+e7bBQCM/Xg8TY86PC5NCFdaNoTLb5Bek5KSePOdFxk2dAQfj/DpAWzudvJXLCLp0KOJ1G5IweplAOQt+IZo/SblkgxrCvdrwCJgNnA/MExEXgWmA+/7dZIgkoc3rN/I2lVraXSo8+DzuPZZLFu8PCG9BkmY/Abp9fmX+rL4+yW89MLr8QllVITUDGc7KZloo+YU/LoaSc1AqjkL9UQbN6dgw+pSREomlCncqvqMiAx1t1eLyFvAKcCrqjrNr/MElTzc9/4B9H3pEZKTk1n50yoe7PVEwnoNIi07bH6D8tr2+FZcfMl5zJ+3iK8mjwCgzyP9GTtmQpm1JLMKqWf1cG4RJELewm/IXzKb3FGvk3Z+T+c2IWcbuR8PLpdXS+H2EZtm7mDTzG2aeSE2zdwwDN+wQmEYhidWKAzD8MQKhWEYnlihMAzDEysUhmF4EtRcj4Tk563r97WFMlEro7L3h8rBuu2bfNcM2yLFqUm+zU38Ex1fXO675oqs+EcGx4tdURiG4YkVCsMwPLFCYRiGJ1YoDMPwxAqFYRiehL5Q+B0o6mug6m4EGX4aiUQYNX4or7/7vC96YQrBDUo3NTWF0eOGMm7S/5gwZSR33XerL7oHH9qQd8a+tutn/Pej6X7dhWUXSkmmxuCXqPnmYGoO+TcVr70KgGjdOtR49SVqDX2bqo89BEnxd26GevZoWQNFgwpU3bIj21NEEAA7AAAK6ElEQVS3POGn9StW99Qt5LqbLueoY5qTWbEC11zSs9TPxtI9mighuLFQHt0aGZVi0s6okMH2bdtJSkpixKdDeODevswqJQ6vYUbNMnsfNeu/XPXXG/llVfFBMx81ipZ4vKSnodk5EI1S45Xn2fTs82RefBHZE74i5/PxVL7r7+xcspTt/xtR7PH1Jo/f/2ePBhEo6nugaoBeC6lTrzYnd+nA+0M+9EUPwhWCG2Tbbt+2HYDk5CSSkpN9jwNs3b4VK39aXWKR8EKznSxXSUqCpCgopLRqSc54JzNj++jPSOvQLm6fgRUKETlURO4UkedEpL+I3Cgivo4gKi5QtF69Or7p+xKo6hKk14efuJsnHxlAQUGBL3pBEVQbBNm2kUiEzyd+yLwfJvHV+Ml8O3OuL7qFdDnnZD4bHkfeRCRCzTdepfYn/yN3+kzyV61Ct26FfOe7kL9uPdGaNeL2GVRc/23AK0Aa0BpIBxoCU0Sko4/n2WOfXxXft0BVl6C8ntylAxt+3ci8OQvj1gqaoNogyO9BQUEBp7T/Gy2bd6JlqxYc0bR8mZbFkZScRIcuJzJu5Ph4DLL+qutZe+6FpDQ9gqRGB+/5GR/aIqgh3NcDx6hqvogMAEapakcR+RfwESUkcYtID6AHgEQr4xWwG1SgaBCBqkF5zTruGE7p2pGOp7QjNTWVihUr8OwrT9Lrxt5xa/tNUG2wN4KAN2/awuRJ0+jUuR2L4nymUsgJJ7dl0Xc/sPHX8q3lURTduo3cb2eT3LwZkpkJ0QjkFxCtVZP8XzfErR/kM4rCIpSKk8aNqq7AxxTuoAJFfQtULUJQXvv1GUjbFqfSruXp9Lz+biZPnJaQRQKCa4OgdKtXr0qlyhUBSEtLpf1Jx7Pkhx/j1i3ktHM7M2b45+U+PlKlMpLp/jtJSSE1qxV5y39ix6xvSet0EgAZp59GzsSv4/Ya1BXFYGC6iEwFOgBPAYhITWCjXycJIlDUz0DVoL0GSZhCcIPSrVWnJgNf7ks0GiUiEUYM/5Sxn30Zty5Aanoqbdpn8eTdT5dbI1K9OlUfvBciEYhEyB73JbmTp5K3/CeqPvYglXpcy87FP7B95Ki4/QbWPSoizYGmwDxVXVTW44MI1w0qUDWW7tHyUJbu0bJgs0dj7x4tK2XtHo2F0rpH4yXW7tHAppmr6nxgflD6hmHsPUI9jsIwjL2DFQrDMDyxQmEYhidWKAzD8MQKhWEYnlihMAzDG1UN/Q/Q40DXDZPXsOmGyWtQuvvLFUUP0w2V17DphslrILr7S6EwDCNArFAYhuHJ/lIoBpluqLyGTTdMXgPRTdjMTMMwEof95YrCMIwACX2hEJGuIvK9iCwRkXt90nxdRNaJyDw/9FzNhiIyXkQWish8EbndJ900EZkmInNc3Uf90HW1oyLyrYh87KPmchH5TkRmi8gMH3WriMh/RGSR28bH+6D5F9dn4c9mEenlk9+/u/+/5onIeyKS5oPm7a7efL987iKIfty99QNEgaXAIUAKMAdo5oNuB+BYnCwNv7zWBY51tysCi33yKkCmu50MfAO09cnzHcC7wMc+tsNyoEYA34U3gevc7RSgSgDftV+Ag33Qqg/8CKS7rz8AropT80hgHpCBEx/xOdDEr//+sF9RtAGWqOoyVd0BvA+cE6+oqn6Fj0lcruYaVZ3lbm8BFuJ8YeLVVVUtTP9Ndn/ifvAkIg2Av+KklSU0IlIJp7i/BqCqO1TV37UGoDOwVFV/8kkvCUgXkSScf9yrPT7vRVNgqqpuV9U8YAJwXpyauwh7oagP/Fzk9Up8+McXNCLSCCdg+Buf9KIiMhtYB4xVVT90nwXuBvxeA0CBMSIy0w1T9oNDgPXAv91bpcEi4h26WjYuBt7zQ0hVVwFPAyuANcAmVY035HMe0EFEqotIBnAGTvK9L4S9UBQX45XQ3Tgikgn8F+ilqpv90FTVfFU9BmgAtBGRI+P0eCawTlVn+uFvN05U1WOB04FbRKSDD5pJOLeKL6tqS2Ab4MvzKgARSQHOBob5pFcV58q3MVAPqCAil8WjqaoLcbJpxwKf4tyG+5ZPGPZCsZI/V80GxH8JFxgikoxTJN5RVf+W9XJxL7e/BLrGKXUicLaILMe5nTtZRIbEqQmAqq52f68D/odz+xgvK4GVRa6k/oNTOPzidGCWqvq1BsApwI+qul5VdwIfAifEK6qqr6nqsaraAefW2Z91BQh/oZgONBGRxm7VvxgofpHFfYw4q9S8BixU1QE+6tYUkSrudjrOl7DMYcZFUdX7VLWBqjbCadMvVDWuv3iuvwoiUrFwG+iCc8kcF6r6C/CziPzF3dUZWBCvbhG649Nth8sKoK2IZLjfi844z6ziQkRqub8PAv6Gj54DC9fdG6hqnojcCnyG81T6dXVCfeNCRN4DOgI1RGQl8LCqvhan7InA5cB37vMEgN6qGm+Wel3gTRGJ4hT+D1TVt+5Mn6kN/M9d2SsJeFdVP/VJuyfwjvsHYxlwtR+i7v3+qcANfugBqOo3IvIfYBbO7cG3+DOa8r8iUh3YCdyiqvGvLORiIzMNw/Ak7LcehmHsBaxQGIbhiRUKwzA8sUJhGIYnVigMw/DECoWBiGx1f9dzu+1K+2wvt8uwLPod/ZiB6peOUXasUOynuOMqyoSqrlbVCzw+1gtnEpNxAGGFImSISCM3c+FNEZnrZjBkuO8tF5GHRGQScKGIHCoin7oTsCaKyBHu5xqLyBQRmS4ifXbTnuduR0XkaTc7Yq6I9BSR23DmJowXkfHu57q4WrNEZJg7l6UwJ2SR6+VvJfy3fCMizYu8/lJEWolIGxGZ7E7wmlxkxGXRYx8RkTuLvJ7nTrZDRC4TJ6Njtoj8qzxF0/gzVijCyV+AQap6FLAZuLnIezmq2k5V38cZ7ddTVVsBdwIvuZ95DmcCVWucjIXi6IEzaamle553VHUgzlyaTqraSURqAA8Ap7gTvWYAd7ghLK8CZwHtgTolnON94CIAEakL1HMnoi0COrgTvB4Cnoy1YUSkKdANZ/LZMUA+cGmsxxvFE+oh3AcwP6vq1+72EOA2nGnLAENh1yzVE4Bh7pBpgFT394nA+e722zizDnfnFOAVN9sAVS0un6Mt0Az42j1HCjAFOAJn0tMPrpchFL/WxAc4sx0fxikYhbMzK+MMS2+CMxs4ubhGKIHOQCtguuspHWf6vREHVijCye7j7ou+3ub+jgC/u39VY9HYHYnxM2NVtfufdoocE8OxqOoqEdkgIkfhXAUUzqfoA4xX1fPc24kvizk8jz9fERdGyQnwpqre53V+I3bs1iOcHCR/ZEJ2Bybt/gE36+JHEbkQnNmrInK0+/bXOLNCoeTL8jHAjeIkMCEi1dz9W3Ci/ACmAieKyGHuZzJE5HCcW4fGInJoEY8l8T5OQE5lVf3O3VcZWOVuX1XCcctxp5KLyLE4t0kA44ALisykrCYiB5dyfiMGrFCEk4XAlSIyF6gGvFzC5y4FrhWROcB8/ogJvB0nNGY6zj/K4hiMMx16rnv8Je7+QcBoERmvqutx/iG/53qZChyhqjk4txqfuA8zS4uP+w9O0fqgyL5+QF8R+RpnVnBx/Beo5s7EvQkngxRVXYDz3GSM62kszgxbIw5s9mjIcC/FP1bVuFKsDKMs2BWFYRie2BWFYRie2BWFYRieWKEwDMMTKxSGYXhihcIwDE+sUBiG4YkVCsMwPPl/It99MBNbQn8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfc59208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = confusion_matrix(ytest, y_model)\n",
    "sns.heatmap(mat, square=True, annot=True, cbar=False)\n",
    "plt.xlabel('predicted value')\n",
    "plt.ylabel('true value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，误判的主要原因在于许多数字 2 被误判成了数字 1 或数字 8。另一种显示模型特征的直观方式是将样本画出来，然后把预测标签放在左下角，用绿色表示预测正确，用红色表示预测错误:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a01518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, 10,  # 10 * 10 的画布\n",
    "                         figsize=(8, 8), # 图片大小8 x 8\n",
    "                        subplot_kw={'xticks':[],'yticks':[]}, # 不显示横纵坐标标签\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1) # 水平和垂直间距\n",
    "                        )\n",
    "x_test_images = Xtest.reshape(-1, 8, 8)\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(x_test_images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(y_model[i]),\n",
    "            transform=ax.transAxes,color='green' if ytest[i] == y_model[i] else 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3超参数与模型验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型验证（model validation）就是在选择模型和超参数之后，通过对训练数据进行学习，对比模型对已知数据的预测值与实际值的差异。  \n",
    "   \n",
    "**1.错误的模型验证方法**  \n",
    "让我们再一次使用鸢尾花数据演示一个简单的模型验证方法，首先加载数据：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后选择模型参数，这里使用$k$近邻分类器，超参数$n\\_neighbors=1$。这是一个非常直观的模型，“新数据的标签与其最接近的训练数据的标签相同”："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后训练模型，并用模型来预测已知标签的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X, y)\n",
    "y_model = model.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后计算模型的准确率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y, y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "准确率是100%？？显然这是不可能的，原因是我们使用**同一套数据集训练和评估模型。**，另外，最近邻模型是一种与距离相关的评估器，只会简单地存储训练数据，然后把新数据与存储的已知数据进行比较来预测标签。在理想情况下，模型的准确率总是100%。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.模型验证的正确方法：留出集**  \n",
    "&emsp;&emsp;留出集指的是从数据集中留出一部分数据用于检验模型的性能。在Scikit-Learn中，可以使用$train\\_test\\_split()$方法来实现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X1, X2, y1, y2 = train_test_split(X, y, random_state=0, \n",
    "                                  train_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用模型拟合训练数据\n",
    "model.fit(X1, y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9066666666666666"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用测试集来评估模型性能（如准确率）\n",
    "y2_model = model.predict(X2)\n",
    "accuracy_score(y2, y2_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的留出集（holdout set）类似新数据，因为模型没有“接触”过他们。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.交叉验证**  \n",
    "&emsp;&emsp;留出集的一个缺点是模型失去了一部分训练数据，在上面的例子中，有一半数据没有对模型做出贡献，这显然不是最优解，当数据集规模较小时这种问题会更加明显。  \n",
    "  \n",
    "解决这个问题的方法是**交叉验证**，也就是做一组拟合，让数据的每个子集既是训练集又是验证集。如图所示：\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图进行两轮验证实验，轮流用一半数据作为留出集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.96, 0.9066666666666666)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2_model = model.fit(X1, y1).predict(X2)\n",
    "y1_model = model.fit(X2, y2).predict(X1)\n",
    "accuracy_score(y1, y1_model),accuracy_score(y2, y2_model)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样可以获取两个准确率，将两者结合（如去均值），可以获得一个更准确的模型总体性能。下图是一个五轮交叉验证：\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把数据分成五组，每一轮依次使用模型拟合其中的四组数据，再预测第五组数据，评估模型的准确率。Scikit-Learn提供了$cross\\_val\\_score()$方法方便实现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "cv = cross_val_score(model, X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对数据的不同子集重复进行交叉检验，可以让我们对算法的性能有更好的认识。\n",
    "Scikit-Learn 为不同应用场景提供了各种交叉检验方法，都以迭代器（iterator）形式在cross_validation $模块^2$中实现。例如，我们可能会遇到交叉检验的轮数与样本数相同的极端情况，也就是说我们每次只有一个样本做测试，其他样本全用于训练。这种交叉检验类型被称为 LOO（leave-one-out，只留一个）交叉检验，具体用法如下：\n",
    ">注 2：从 Scikit-Learn 0.18 版起，开始用 model_selection 模块代替 cross_validation 模块，并计划在0.20 版移除程序包，部分交叉检验类的用法也可能会发生变化。本书按照作者原文保留 cross_validation ，建议读者使用作者建议的 Scikit-Learn 版本运行本书代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import LeaveOneOut\n",
    "scores = cross_val_score(model, X, y, cv=LeaveOneOut(len(X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于我们有 150 个样本，留一法交叉检验会生成 150 轮试验，每次试验的预测结果要么成功（得分 1.0），要么失败（得分 0.0）。计算所有试验准确率的均值就可以得到模型的预测准确性了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.3.2选择最优模型**  \n",
    "如何选择模型和超参数是机器学习实践中最重要的内容。同时**如果模型的效果不好，应该如何去改善呢？**答案可能有以下几种：  \n",
    "（1）用更复杂/更灵活的模型  \n",
    "（2）用更简单/更确定的模型 \n",
    "（3）采集更多的训练样本  \n",
    "（4）为每个样本采集更多的特征"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.偏差与方差的平衡**  \n",
    "“最优模型”的问题基本可以看是找出“偏差”与“方差”平衡点的问题。如下图是同一数据集两种不同的回归模型：  \n",
    "![image.png](attachment:image.png)  \n",
    "显然，这两个模型拟合的效果都不是很好，但是它们的问题却是不一样的。  \n",
    "  \n",
    "左边的模型希望从数据中找到一条直线，但是由于数据本质上比直线更加复杂，直线永远不可能很好地描述这份数据，这样的模型称为**欠拟合**；也就是说模型没有足够的灵活性来适应所有的特征，另一说法是模型具有**高偏差**。    \n",
    "右边的模型希望用高阶多项式拟合数据，尽管这个模型几乎完美地适应了数据的所有特征，但与其说它十分准确地描述了训练数据，不如说它是过多地学习了数据的噪声，而不是数据的本质属性，这样的模型被认为是对数据**过拟合**，也就是模型过于灵活，在适应数据所有特征的同时，也适应了随机误差，另一说法是模型具有**高方差**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.Scikit-Learn验证曲线**  \n",
    "来看一个例子，用交叉检验计算一个模型的验证曲线。这里使用**多项式回归**模型。其多项式的次数是一个可调参数。例如，多项式次数为1其实就是将数据拟合成一条直线。若模型具有参数$a$和$b$，则模型为：  \n",
    "$$y = ax + b$$  \n",
    "多项式次数为3，则是将数据拟合成一条三次曲线。如模型有参数$a$、$b$、$c$和$d$，则模型为：  \n",
    "$$y = ax^3 + bx^2 + cx + d$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "推而广之，就可以得到任意次数的多项式。在Scikit-Learn中，可以用一个带多项式预处理器的简单线性回归模型实现。我们将用一个**管道命令**来组合这两种操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PolynomialRegression(degree=2, **kwargs):\n",
    "    return make_pipeline(PolynomialFeatures(degree),\n",
    "                        LinearRegression(**kwargs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创造一些数据用于模型拟合\n",
    "def make_data(N, err=1.0, rseed=1):\n",
    "    rng = np.random.RandomState(rseed)\n",
    "    X = rng.rand(N, 1) ** 2\n",
    "    y = 10 - 1.0 / (X.ravel() + 0.1)\n",
    "    if err > 0:\n",
    "        y += err * rng.randn(N)\n",
    "    return X, y\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_data(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x149005c0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x149004e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test = np.linspace(-0.1, 1.1, 500)[:, np.newaxis]\n",
    "\n",
    "plt.scatter(X.ravel(), y, color='black')\n",
    "axis = plt.axis()\n",
    "for degree in [1, 3, 5]:\n",
    "    y_test = PolynomialRegression(degree).fit(X, y).predict(X_test)\n",
    "    plt.plot(X_test.ravel(), y_test, label='degree={}'.format(degree))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个例子中控制模型复杂度的关键是多项式的次数，它只要是非负整数就可以。那么问题来了：究竟多项式的次数是多少，才能在偏差（欠拟合）与方差（过拟合）之间达到平衡？  \n",
    "我们可以通过可视化验证曲线来回答这个问题--利用Scikit-Learn的$validationg\\_curve()$函数来实现，只要提供模型，数据，参数名称和验证范围信息，函数就会自动计算验证范围内的训练得分和验证得分："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\learning_curve.py:22: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'score')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14998a90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.learning_curve import validation_curve\n",
    "degree = np.arange(0, 21)\n",
    "train_score, val_score = validation_curve(PolynomialRegression(), \n",
    "                                          X, y,\n",
    "                                          'polynomialfeatures__degree',\n",
    "                                         degree, cv=7)\n",
    "plt.plot(degree, np.median(train_score, 1), color='blue', \n",
    "         label='training score')\n",
    "plt.plot(degree, np.median(val_score, 1), color='red', \n",
    "         label='validation score')\n",
    "plt.legend(loc='best')\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel('degree')\n",
    "plt.ylabel('score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出：训练得分总是高于验证得分，训练得分随着模型复杂度的提升而单调递增，验证得分增长到最高点后由于过拟合而骤然下降。  \n",
    "  \n",
    "从验证曲线可以看出，方差与偏差均衡性最好的是三次多项式。我们可以计算结果，并将模型画在原始数据上："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IZ44kdmPMD4CvARbYAlxnrW124twSGmt2H+GOF7ewq7KBC2YM57YLpjJ26PGjN/2SSn9FW5lgrAg4sRtjCoHvAtOttU3GmGeAK4FHAz23BN/RhlZ++do2nlt/kFGD03nk2lOOK6/rSr+kAxPP01fRViYYK5yaikkC0o0xLiADKHPovBJEr390mB+/uIXaJhc3nTmB75w9qddNevVL2n/xPn0VbWWCsSLgGiJrbSnwK2A/cAiosdb+PdDzSvDUNrv44TObWP74ekbmpvHqdz/HredP7XPnde3h2X+9TV/FgyVzCrlz6UwKc9MxeJfgh7ptQzxyYipmMHAZMA6oBp41xlxlrX2823E3AjcCjBkzJtCnlQFas/sIP3xmE4dqmvjO2RP5ztmTSEny7//3aKvljQSavorsFb6xyompmHOAPdbaSgBjzAvAAuC4xG6tfQB4ALx17A48r/RDm8fy27d2cO87JYwdksFz31zAyWNObDvbF/2S9o+mryQcnFjOtR84zRiTYbwFrIuAbQ6cVxxSVd/C1Q+v5ferSvji3FG89r3PDSipS/9p+krCIeARu7V2rTHmOeBDwA1soH1kLuFXvPco3/rrh1Q3urj78llcMW90uEOKK5q+knBQS4EYZa3lkdV7+Y/XtjF6cDr3L5vL9JHZ4Q5LRAKglgIxpj+10K42D//7pY95ct1+zps+jF9dMZvsMGw/F4sisSY9EmOS8FJijwL9qYWuaXRx01/Xs7rkCDedOYGbz5tCghorOSISa9IjMSYJv+jZFSGO+VsLve9IA1/4w2rW7TnKr744m1vPn6qk7qBIrEmPxJgk/DRijwL+1EJv2H+M6x/9AAs8fsOpnDp+6ICeS2/rfYvEmnSnYtLrHls0Yo8CvmqeO77+r51VLHtwLdnpyay4aWFASf32F7ZQWt2E5dO39Ss2lA409JjS1+sQDk7EpNc99iixR4HeaqH/tuUQ1z/6AWOGZPDs8tMpGkA/7Q56W9+7SKxJdyImve6xR1MxUcBXLXSLu41/e2Yjc8YM5uFrTiEnI7DKl0icaogkkViT7kRMet1jjxJ7lOi+lP/Bf+7mF69u44zJ+fzhqpPJSAn8pdTy975FYkuFQGPS6x57NBUThR761x5+8eo2Lpw5nD9dPc+RpA6ROdUgwafXPfZoxB5lHl29h5+/spULZgznd1fOIdnB3dsjcapBgk+ve+xRS4Eo8pf39/LTlz7mvOnDuG/ZyY4mdRGJfP62FFBmiBJPrN3HT1/6mHOmFXDvV5TURcQ3ZYco8NLGUu548SPOnlrAfctO9ntjDBGJT8oQEe69HZXc/Owm5o8bwv3LTiY1qfft60RElNgj2KYD1Sx/fD0T8jP509XzSEtWUheRvqkqJkLtqqznukc/YMigFB67fj456Wq7G+/Uz0X8pcQegcprm7n6oXUY4C83nEpBdlrIY1ASiSxqzyv9ocTeD8FMdh3nLq1uIjnRYIzh+eULGBdA75dAYlESiSy99XPRayLdaY7dT8HsgNf13ACuNgvWOx0TDmoKFXnUz0X6Q4ndT8FMdj2du7XNE7ZEqiQSeSKxZbBELiV2PwUz2UVaIlUSiTzq5yL9ocTup2Amu6GZKUE790AoiUSeJXMKuXPpTApz0zFAYW46dy6dqfl16ZFunvrplsVTjruhCJ8mu0BuqpbXNtPi9mCArl17wplI1RQqMkViy2CJTErsfvKV7IABV5C42jx864kPcbdZbj1/Co+v2R8xiVRJRCR6KbH3Q0/JbuFdqwZchvbL17ZRvO8Y//XlOVw6eyQjctI7/+PouHGq5Coi/eXIHLsxJtcY85wx5hNjzDZjzOlOnDcaDPTG58pNZTyyei/XLiji0tkjtaGwiDjGqZunvwNet9ZOBWYD2xw6b8QbyE3VneV13Pb8ZuaNHcyPL5wGqHZcRJwTcGI3xmQDnwceArDWtlprqwM9b7TobwVJfYubZQ+updnVRvG+Y5z1q3dZsaE04koeRSR6OTFiHw9UAo8YYzYYYx40xpywDt4Yc6MxptgYU1xZWenA00aG/pahXffIOirqWvC0l8B0TLnkZvTc5Eu14yLSXwFvjWeMmQesARZaa9caY34H1Fprf+rre+J1a7wVG0r5/tMbe3wsNz2ZFrfnhHJK1SqLSIdQbo13EDhorV3b/vlzwMkOnDem7D/SyE9WfOTz8ZomlxagiIgjAk7s1trDwAFjTMek8iJga6DnjSWuNg/feWoDxsCw7NQej8nNSNaCIBFxhFNVMd8BnjDGbAZOAn7p0Hljwm/e3MGmA9XctXQWt18w7YSbrcmJhvpm93Gljj94eiM/WbElPAGLSFRzZIGStXYj0Oe8Tzz6n11V/OEfu/jSvNFcNGtE59e7js4bWtxUN7mO+z4LPLFmP/PGDtHIXUT6JeCbpwMRLzdPa5tdnP+b90hLTuSV736WjJSe/x8dd9ur+HoVCnPTWX3b2cELUkSihr83T9VSoBeB7pj0i1e2cri2mee/ucBnUgdvSWOp6thFxCFq2+tDoEv8V31SzjPFB1l+xgTmjBnc67G3LJ6C8fGY6thFpL+U2H0IZIl/TaOL257fwpRhWXzvnEl9Hr9kTiHLThtzQnJXD3QRGQgldh8CWeL/s5c/5mhDK/95xWxSkxL7PB7gF0tm8psvnaQ6dhEJmObYffA1793X1MgbHx/mxQ2lfG/RJGYU5vTrOdUDXUScoBG7DwPZHq66sZU7XvyI6SOy+fbZE4MdoohIjzRi92Eg28Pd+donHGts5bHr55OcqP8zRSQ8lNh70Z+pkTW7j/B08QGWnzGB6SOzgxyZiIhvGlY6oNnVxo9f3MLoIel8b1HfVTAiIsGkEbsD7n93F7srG3js+vmkp/hXBSMiEiwasQeopKKOP7xbwpKTRvL5yfnhDkdERCN2f3S0FiitbiLRGNqspTA3nR+eN5kn1+1nUGoSP7l4erjDFBEBlNh71LVHTG5GMvXNblzte9m1tTdNK61u4kfPb8bVZrn78lnkZfbcZ11EJNSU2Lvp6BHT0U7gWKPL57GuNktKYgJfnDsqVOGJiPRJc+zd9NQjpjetbR6M8dXCS0Qk9JTYu+lvm9xCdV8UkQijxN5Nf9rkpiUlqPuiiEQcJfZueuoRk5xoyE1PBuhsrZubnsxd/2uWmnaJSMTRzdNuOhL1z1Z+3LkPaWZqEv9+yWc4d/owzv7Pd8nPSuWlb32WxATNrYtI5FFi96HF7en8+Fiji9tf2MLLm8sor23h/mVzldRFJGLFfGIfyL6lvnZPentbBUvnFDJ3bO9b3YmIhFNMJ/buNekd+5YCvSb33ipjbjlfN0tFJLLF9M3Tge5b6qsyJis1iRE5Km8UkcjmWGI3xiQaYzYYY15x6pyBGui+pT1VxgDccdE0R+ISEQkmJ0fs3wO2OXi+gPkaefdVq75kTiF3Lp153OKjL84dxZXzxzgan4hIMDiS2I0xo4CLgAedOJ9TBrJvaYclcwp595YzGZc3iIkFmdy5dGawwhQRcZRTN09/C9wKZDl0PkcMZN/Srp5Ys489VQ08fO08krSHqYhEiYATuzHmYqDCWrveGHNmL8fdCNwIMGZM6KY0+rNvaVe1zS5+9/ZOFk4cyllTCoIQmYhIcDgxDF0IXGqM2Qs8BZxtjHm8+0HW2gestfOstfPy8yN/p6EH/rHbuzDpgmnq3igiUSXgxG6tvd1aO8paWwRcCayy1l4VcGRhVFXfwsOr93DxrBHMKMwJdzgiIv2iieMe3P/OLppdbfzg3MnhDkVEpN8cXXlqrX0XeNfJc4ZaWXUTj6/dx+VzRzEhPzPc4YiI9JtG7N38ftVOrLV8d9GkcIciIjIgSuxd7Klq4Jnigyw7dSyjBmeEOxwRkQFRYu/it2/tIDnRcNNZE8IdiojIgCmxt9t2qJaVm8q4buE4CrLSwh2OiMiAKbG3+82bO8hMSeIbnx8f7lBERAKixA5sLavl71vLuf6z48jNSAl3OCIiAVFiB+59ZydZqUlcv3BcuEMREQlY3Cf27YfreG3LYa5dWERORnK4wxERCVhMb43Xla+9T+99p4RBKYkarYtIzIiLxO5r79Py2mZe2VzG8jMmMHiQ5tZFJDbExVSMr71Pf/vWTtKSEvnaZzVaF5HYEReJ3dcep02uNr56+liGZqaGOCIRkeCJi8Te2x6nX/+c6tZFJLbERWLvae9TgM9PyiM/S6N1EYktcXHztPvepxkpiTS7Pdx9+ewwRyYi4ry4SOzw6d6nR+pbWPj/VrF0TiHDc9QTRkRiT1xMxXT15/f30ezy8I0zNLcuIrEpLkbsHYuTSqubMAZmFGYzsSAr3GGJiARFzI/YOxYnlbaXPFoLOw7Xs2JDaZgjExEJjphP7D0tTmpt83DPG9vDFJGISHDFfGL3tTjJ19dFRKJdzCd2X4uTelu0JCISzWI+sd+yeAopicdfZnpyIrcsnhKmiEREgivmE/uSOYWMHZpBojEAFOamc+fSmZ2LlkREYk3A5Y7GmNHAY8BwwAM8YK39XaDndcrWslp2VtRz2wVTWX7GhHCHIyISdE7UsbuBH1prPzTGZAHrjTFvWmu3OnDugD28eg/pyYl8+ZQx4Q5FRCQkAk7s1tpDwKH2j+uMMduAQiDkib37LknLzxjPyo1lXDl/tLa9E5G44ejKU2NMETAHWOvkef3R0y5J/+flrbg9lmsXFIU6HBGRsHHs5qkxJhN4Hvi+tba2h8dvNMYUG2OKKysrnXraTj0tRHJ7LGlJCYzPz3T8+UREIpUjid0Yk4w3qT9hrX2hp2OstQ9Ya+dZa+fl5+c78bTH8bXgqNntcfy5REQiWcCJ3RhjgIeAbdbaXwce0sD4XIik1rwiEmecGLEvBL4KnG2M2dj+50IHztsvPe2SlJxouPX8qaEORUQkrJyoivkXYByIJSBdd0kqrW4iwcAvv6CFSCISf2Jq5emSOYU8+fXTMAbOmTaM3761k3G3vcrCu1apTa+IxI2Y22jjibX7MMB7Oyo7b5yWVjdx+wtbADSCF5GYF/WJveuipBE5aVQ3uUhJSqDZdXw1TJOrjXve2K7ELiIxL6oTe/dFSWU1zb0erx7sIhIPonqOvadFSb1RD3YRiQdRndj7MwJXD3YRiRdRndj7GoEnGoNBPdhFJL5E9Rz7LYunHDfH3p3HWvbcdVGIoxIRCa+oTOxdK2HSk32/6dCcuojEo6hL7N0rYRpdPTf50py6iMSrqJtj96cSJtEYzamLSNyKusTuTyWMx1oldRGJW1GX2P2ZN9fcuojEs6hL7D215+3KtB8jIhKvoi6xL5lTyJ1LZ5Kb3vN936QE+MHTG9XRUUTiVtQldvAm999/5eQeH3N5wPJpR0cldxGJN1GZ2AGeXLefhD629+jo6CgiEk+iMrFX1bfw5tZyPLbvY9XRUUTiTVQm9ufXH8TVZinISu3zWFXIiEi8ibrEbq3lqQ8OcErRYH584bReK2S0+lRE4lHUtRRYs/soe6oa+PZZE4/bwLqsuoncjGSshZomFyNz07ll8RQtVBKRuBN1if3pD/aTlpzAr/6+nZuf3aQELiLSTVQl9tpmF69uOYTHwqH2bfC0UbWIyPGiao79lU2HcLVZ2rqVw6isUUTkU1GV2J9bf8DnYyprFBHxciSxG2PON8ZsN8aUGGNuc+Kc3e2qrOfD/dVkp/U8e6SyRhERr4ATuzEmEbgPuACYDnzZGDM90PN29/z6gyQmGH543olNwFTWKCLyKSdG7POBEmvtbmttK/AUcJkD5+3U5rG88GEpZ0zO55oFRdy5dCaFuenaqFpEpAdOVMUUAl0nvw8Cp3Y/yBhzI3AjwJgxY/r1BP8qqeJwbTP/+xLvG4ElcwqVyEVEfHBixN5TK64TurhYax+w1s6z1s7Lz8/v1xM8t/4guRnJLJpWMNAYRUTihhOJ/SAwusvno4AyB84LeFeRvvHxYS6bPZLUJN/tA0RExMuJxP4BMMkYM84YkwJcCax04LwAvLK5jFa3h8vnju77YBERCXyO3VrrNsZ8G3gDSAQettZ+HHBk7Z5bf5Cpw7OYUZjt1ClFRGKaIy0FrLWvAa85ca6udlXWs2F/NXdcOA1j+ti1vJIuAAAHB0lEQVRVQ0REgAhfebpiQykJBi6bMzLcoYiIRI2ITezWWlZsLGXhxDwKstLCHY6ISNSI2O6OH+4/xoGjTXx/0eQTHluxobSzB7va9oqIHC9iE/uLG0pJS05g8Yzhx319xYZSbn9hC02uNkBte0VEuovIqZhWt4dXNx/i3OnDyUw9/v+ee97Y3pnUO6htr4jIpyIysb+3o5JjjS6+0MNNU1/tedW2V0TEKyIT+4qNpQwZlMLnJp3YesBXe1617RUR8Yq4xF7X7OLNreVcPGsEyYknhnfLYrXtFRHpTcTdPH39o8O0uD0+b4R2fF1VMSIiPYu4xP7SxjLGDs1gzuhcn8eoba+IiG8RNRVTXtvM6l1VXHZSoVoIiIgMUEQl9pc3lWEtLDlJLQRERAYqohL7yk1lzCzMYXx+ZrhDERGJWhGT2PcdaWDzwRoumT0i3KGIiES1iEnsr2w+BMBFszQNIyISiIipinl5Uxlzxw6m0I+FRmoCJiLiW0SM2Esq6vnkcB0Xz+p7GqajCVhpdROWT5uArdhQGvxARUSiQEQk9lc2l2EMXDiz78SuJmAiIr0Le2K31vLypjLmFw1hWHbfG2qoCZiISO/Cntg/OVzHrsoGLpnt301TNQETEeld2BP7K5vLSEwwXNBtQw1f1ARMRKR3Ya2KsdbyyuZDLJgwlKGZqX59j5qAiYj0LqyJfUtpDfuONHLTmRP69X1qAiYi4ltYp2Je2XyI5ETD4s/4Nw0jIiJ9CyixG2PuMcZ8YozZbIx50Rjju9duN9ZaXt18iM9Nyic3IyWQMEREpItAR+xvAjOstbOAHcDt/n7jpoM1lFY3+VW7LiIi/gsosVtr/26tdbd/ugYY5e/3/u2jQyQlGM6dNiyQEEREpBsn59ivB/7m60FjzI3GmGJjTHFlZSWvf3SYBRPzyMlIdjAEEREx1treDzDmLaCnu5t3WGtfaj/mDmAesNT2dULv8ZXAvv6He4I8oMqB80QTXXN80DXHj/5c91hrbX5fB/WZ2Ps8gTHXAMuBRdbaxoBO1v/nLrbWzgvlc4abrjk+6JrjRzCuO6A6dmPM+cCPgDNCndRFRKRngc6x3wtkAW8aYzYaY/7oQEwiIhKAgEbs1tqJTgUyQA+E+fnDQdccH3TN8cPx6w54jl1ERCJL2Ls7ioiIs6IisRtjzjfGbDfGlBhjbuvh8VRjzNPtj681xhSFPkpn+XHN/2aM2drezuFtY8zYcMTppL6uuctxlxtjrDEm6iso/LlmY8wV7a/1x8aYv4Y6Rqf58bM9xhjzjjFmQ/vP94XhiNNJxpiHjTEVxpiPfDxujDH/1f5vstkYc3JAT2itjeg/QCKwCxgPpACbgOndjrkJ+GP7x1cCT4c77hBc81lARvvH34yHa24/Lgt4D+9K53nhjjsEr/MkYAMwuP3zgnDHHYJrfgD4ZvvH04G94Y7bgev+PHAy8JGPxy/Eu8DTAKcBawN5vmgYsc8HSqy1u621rcBTwGXdjrkM+HP7x88Bi4wxJoQxOq3Pa7bWvmM/LTHtVzuHCOXP6wzwc+BuoDmUwQWJP9f8deA+a+0xAGttRYhjdJo/12yB7PaPc4CyEMYXFNba94CjvRxyGfCY9VoD5BpjBtxIKxoSeyFwoMvnB9u/1uMx1tu7pgYYGpLogsOfa+7qBnpp5xAl+rxmY8wcYLS19pVQBhZE/rzOk4HJxpjVxpg17WtHopk/1/wz4CpjzEHgNeA7oQktrPr7O9+rsG604aeeRt7dS3n8OSaa+H09xpir8LZzOCOoEQVfr9dsjEkAfgNcG6qAQsCf1zkJ73TMmXjflf3TGDPDWlsd5NiCxZ9r/jLwqLX2P40xpwN/ab9mT/DDCxtHc1g0jNgPAqO7fD6KE9+adR5jjEnC+/att7c9kc6fa8YYcw5wB3CptbYlRLEFS1/XnAXMAN41xuzFOw+5MspvoPr7s/2StdZlrd0DbMeb6KOVP9d8A/AMgLX2fSANbz+VWObX77y/oiGxfwBMMsaMM8ak4L05urLbMSuBa9o/vhxYZdvvSESpPq+5fVriv/Em9Wifd4U+rtlaW2OtzbPWFllri/DeV7jUWlscnnAd4c/P9gq8N8oxxuThnZrZHdIoneXPNe8HFgEYY6bhTeyVIY0y9FYCV7dXx5wG1FhrDw34bOG+W+znHeUL8W7ksQtvV0mA/4v3Fxu8L/yzQAmwDhgf7phDcM1vAeXAxvY/K8Mdc7Cvudux7xLlVTF+vs4G+DWwFdgCXBnumENwzdOB1XgrZjYC54U7Zgeu+UngEODCOzq/AW/zxOVdXuf72v9NtgT6s62VpyIiMSYapmJERKQflNhFRGKMEruISIxRYhcRiTFK7CIiMUaJXUQkxiixi4jEGCV2EZEY8/8BAPAkZZ55A2cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x149beeb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X.ravel(), y)\n",
    "lim = plt.axis()\n",
    "y_test = PolynomialRegression(3).fit(X, y).predict(X_test)\n",
    "plt.plot(X_test.ravel(), y_test)\n",
    "plt.axis(lim);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3.3学习曲线  \n",
    "&emsp;&emsp;影响模型复杂度的另一个重要因素是最优模型往往受到训练数据量的影响。例如生成前面5倍的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x68e3eb8>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147579e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X2, y2 = make_data(200)\n",
    "plt.scatter(X2.ravel(), y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'score')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x690c048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "degree = np.arange(0, 21)\n",
    "train_score2, val_score2 = validation_curve(PolynomialRegression(), \n",
    "                                          X2, y2,\n",
    "                                          'polynomialfeatures__degree',\n",
    "                                         degree, cv=7)\n",
    "plt.plot(degree, np.median(train_score2, 1), color='blue', \n",
    "         label='training score')\n",
    "plt.plot(degree, np.median(val_score2, 1), color='red', \n",
    "         label='validation score')\n",
    "\n",
    "plt.plot(degree, np.median(train_score, 1), color='blue', alpha=0.3,\n",
    "         label='training score', linestyle='dashed')\n",
    "plt.plot(degree, np.median(val_score, 1), color='red', alpha=0.3,\n",
    "         label='validation score', linestyle='dashed')\n",
    "plt.legend(loc='lower center')\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel('degree')\n",
    "plt.ylabel('score')"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实现是大数据集的验证曲线，而虚线是前面小数据集的验证曲线。可以看出，大数据集支持更加复杂的模型；虽然得分项定顶点大概是6次多项式，但是即使到了20次多项式，过拟合情况也不太严重--训练得分和验证得分依然比较接近。  \n",
    "  \n",
    "同时还可以看出，通过观察验证曲线的趋势，可以发现有两个影响模型效果的因素：模型的复杂度和训练数据集的规模。通常可以将模型看成是和训练数据规模相关的函数，通过不断扩大数据集的规模来拟合模型，以此来观察模型的行为。反映训练集规模的训练得分/验证得分曲线被称为**学习曲线**（learning curve）。  \n",
    "  \n",
    "学习曲线的特征包括以下三点：  \n",
    "（1）特定复杂度的模型对较小的数据集容易过拟合：此时训练得分较高，验证得分较低；  \n",
    "（2）特定复杂度的模型对较大的数据集容易欠拟合：随着数据集的增大，训练得分不断减小，验证得分不断增大；  \n",
    "（3）模型的验证集得分总是低于训练集得分：两条曲线一直在靠近，但是永远不会交叉。  \n",
    "如下图所示：  \n",
    "![image.png](attachment:image.png)  \n",
    "  \n",
    "学习曲线最重要的特征是，随着训练样本的增加，分数会收敛到定值，一旦数据已经多到使模型已经收敛，那么增加再多的数据已经无济于事了。此时，改善模型性能的唯一方法是换模型（通常是换复杂的模型）。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf3c1080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.learning_curve import learning_curve\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
    "fig.subplots_adjust(left=0.0325, right=0.95, wspace=0.1)\n",
    "for i, degree in enumerate([2, 9]):\n",
    "    N, train_lc_score, validate_lc_score = learning_curve(PolynomialRegression(degree),\n",
    "                                                     X, y, cv=7,\n",
    "                                                     train_sizes=np.linspace(0.3, 1, 25))\n",
    "    ax[i].plot(N, np.mean(train_lc_score, axis=1), color='blue', label='training score')\n",
    "    ax[i].plot(N, np.mean(validate_lc_score, axis=1), color='red', label='validation score')\n",
    "    ax[i].hlines(np.mean([train_lc_score[-1], validate_lc_score[-1]]), N[0], N[-1], \n",
    "                 color='green', linestyle='dashed')\n",
    "    ax[i].set_ylim(0, 1)\n",
    "    ax[i].set_xlim(N[0], N[-1])\n",
    "    ax[i].set_xlabel('training size')\n",
    "    ax[i].set_ylabel('score')\n",
    "    ax[i].set_title('degree = {}'.format(degree), size=15)\n",
    "    ax[i].legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x5677ba8>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf1ef198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.3.4验证实践： 网格搜索**  \n",
    "在实际应用中，模型通常会有多个得分转折点，因此验证曲线和学习曲线的图形会从二维图形变成多维曲面。这种高维可视化很难展现，因此从图中找出验证得分的最大值不是一件简单的事情。  \n",
    "  \n",
    "Scikit-Learn提供了$grid\\_search()$方法提供了一个自动化工具解决这个问题。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "param_grid = {'polynomialfeatures__degree': np.arange(21),\n",
    "             'linearregression__fit_intercept': [True, False],\n",
    "              'linearregression__normalize': [True, False]\n",
    "             }\n",
    "grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将评估器应用到数据上：  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=7, error_score='raise',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('polynomialfeatures', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('linearregression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))]),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'polynomialfeatures__degree': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20]), 'linearregression__fit_intercept': [True, False], 'linearregression__normalize': [True, False]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'linearregression__fit_intercept': False,\n",
       " 'linearregression__normalize': True,\n",
       " 'polynomialfeatures__degree': 9}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，可以用最优参数的模型拟合数据，并画图显示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py:3259: MatplotlibDeprecationWarning: The 'hold' keyword argument is deprecated since 2.0.\n",
      "  mplDeprecation)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf216b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = grid.best_estimator_\n",
    "plt.scatter(X.ravel(), y)\n",
    "lim = plt.axis()\n",
    "y_test = model.fit(X, y).predict(X_test)\n",
    "plt.plot(X_test.ravel(), y_test, hold=True)\n",
    "plt.axis(lim);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.axis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
